Analysis on Effects of Fault Elements in Memristive Neuromorphic Systems

Nowadays, neuromorphic systems based on Spiking Neural Networks (SNNs) attract attentions of many researchers. There are many studies to improve performances of neuromorphic systems. These studies have been showing satisfactory results. To magnify performances of neuromorphic systems, developing actual neuromorphic systems is essential. For developing them, memristors play key role due to their useful characteristics. Although memristors are essential for actual neuromorphic systems, they are vulnerable to faults. However, there are few studies analyzing effects of fault elements in neuromorphic systems using memristors. To solve this problem, we analyze performance of a memristive neuromorphic system with fault elements changing fault ratios, types, and positions. We choose neurons and synapses to inject faults. We inject two types of faults to synapses: SA0 and SA1 faults. The fault synapses appear in random and important positions. Through our analysis, we discover the following four interesting points. First, memristive characteristics increase vulnerability of neuromorphic systems to fault elements. Second, fault neuron ratios reducing performance sharply exist. Third, performance degradation by fault synapses depends on fault types. Finally, SA1 fault synapses improve performance when they appear in important positions.Comment: 8 pages, 7 figures, 5 tables, IJCAI 2023 GLOW workshop(https://sites.google.com/view/glow-ijcai-23/home

An O.D.E. Framework of Distributed TD-Learning for Networked Multi-Agent Markov Decision Processes

The primary objective of this paper is to investigate distributed ordinary differential equation (ODE) and distributed temporal difference (TD) learning algorithms for networked multi-agent Markov decision problems (MAMDPs). In our study, we adopt a distributed multi-agent framework where individual agents have access only to their own rewards, lacking insights into the rewards of other agents. Additionally, each agent has the ability to share its parameters with neighboring agents through a communication network, represented by a graph. Our contributions can be summarized in two key points: 1) We introduce novel distributed ODEs, inspired by the averaging consensus method in the continuous-time domain. The convergence of the ODEs is assessed through control theory perspectives. 2) Building upon the aforementioned ODEs, we devise new distributed TD-learning algorithms. A standout feature of one of our proposed distributed ODEs is its incorporation of two independent dynamic systems, each with a distinct role. This characteristic sets the stage for a novel distributed TD-learning strategy, the convergence of which can potentially be established using Borkar-Meyn theorem

Effect of Ceramic Dust as Partial Replacement of Cement on Lightweight Foamed Concrete

Disposal of waste into the landfill causes a severe impact on the environment. One of the waste products is ceramic waste. Ceramic waste has some excellent properties in its durability, hardness, and highly resistant to biological, chemical, and physical degradation forces. These excellent properties of the ceramic waste may make it suitable to be used in concrete. This study investigates the effect on the compressive strength of lightweight foamed concrete with different percentage of ceramic dust replacement level towards the cement and three different levels of water-cement ratio. 0%, 5%, 15%, and 25% of replacement level with 0.52, 0.56, and 0.60 water-cement ratios respectively for each replacement level was used as the parameter to investigate the fresh properties, and strength performance of lightweight foamed concrete. The stability and consistency of every mix are studied as well. From this study, it was observed that the incorporation of ceramic waste dust partially replaced the cement did not affect on the fresh properties of the foamed concrete. However, the compressive strength of foamed concrete affected by ceramic waste dust partially replaced the cement